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7 articles for “Mel-Frequency Cepstral Coefficient (MFCC)”
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Automatic Baby Cry Detector with sleep music player (ABCD)
Abstract: In today’s world, our lives have become moredependent on technology. One problem which caught our eyesis when parents have to leave their wards (aged between 3months to 2 years) alone due to some essential tasks for a shortduration, the baby goes unmonitored. Normally, parents dothis by leaving their child asleep. In many cases, the childwakes up and starts to cry. In absence of loved ones, babiesneed immediate calmness relief. A …
Published in Journal of Mechatronics and Automation · Vol. 10, Issue 1, 2023 · pp. 21–29 Read article
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Classifying Abnormalities in Heartbeat Sound
Abstract: Heartbeat sounds play a major role in the detection of various diseases such as heart disease, hyperthyroidism, and high blood pressure in their early stages. In the proposed method, various abnormal and healthy heartbeat audio signals are given as input and the features are extracted using MFCC (mel-frequency cepstral coefficients). Then, a deep learning approach is applied in which the MFCC audio signals are sent to the CNN (convolutional neural …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 12, Issue 1, 2024 · pp. 24–31 Read article
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Development of Polymer-Based Sensors for Speech Emotion Recognition
Abstract: Traditional SER research often utilizes microphones with polymer components like Diaphragms and Membranes. Within some microphone designs, polymer membranes which plays a crucial role in converting sound pressure into electrical signals. The paper highlights the application (speech emotion recognition) and have tried to find polymer-based sensors. This work further delves deeper, investigating the performance of the CatBoost algorithm for emotion recognition in voice assistants designed for Indian languages. The research …
Published in Journal of Polymer & Composites · Vol. 12, Issue 5, 2024 · pp. 268–274 Read article
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Bias Detection and Accuracy Enhancement in Voice-based Banking Authentication Using Deep Learning
Abstract: Biometric systems have become an integral part of how many people access banking services today, and voice verification systems can be a secure and easy-to-use source of banking authentication that does not require any physical contact with the bank or any other person. From the security perspective, these systems would normally provide an effective means of identifying an individual but frequently exhibit bias with respect to demographics such as the …
Published in International Journal of Information Security Engineering · Vol. 4, Issue 2, 2026 Read article
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Optimized Residual Neural Network for Audio Spoof Detection in Speaker Verification Systems
Abstract: The Automatic speaker verification system is a type of biometric technology that utilizes speech to determine if a person is an authentic user or not. Unfortunately, such systems can be susceptible to audio-spoofing attacks. The proposed work deals with this problem of Audio Spoofing by employing Residual Networks to determine if a voice signal is bonafide or not. A comparative study of Mel-frequency Cepstral coefficients (MFCC), Constant Q Cepstral Coefficients …
Published in Current Trends in Signal Processing · Vol. 12, Issue 2, 2022 · pp. 19–32 Read article
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Identification of English Dialects and Emotions using Spectral and Prosodic Features of Speech Signal Processing
Abstract: AbstractIn this paper, the authors have explored speech features to identify English dialects and emotions. A dialect is any distinguishable variety of a language spoken by a group of people. Emotions provide naturalness to speech. Speech database considered for dialect identification task consists of spontaneous speech spoken by male and female speakers. The emotions considered in this study are anger, disgust, fear, happy, neutral and sad. Prosodic and spectral features …
Published in Journal of Computer Technology & Applications · Vol. 4, Issue 2, 2013 · pp. 10–17 Read article
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Exploiting Phase of Speech Signal for Speaker Recognition
Abstract: Abstract—Performance of speaker recognition system with feature based on temporal phase is presented in this paper. In the state of the art spectral feature, Mel Frequency Cepstal Coefficient (MFCC) only magnitude of the Fourier Transform of speech signal is considered while phase is ignored. The cepstral coefficients extracted from temporal phase (MFTPC) of speech signal are used as features for speaker recognition system. The performance of MFTPC feature is evaluated …
Published in Journal of Communication Engineering & Systems · Vol. 9, Issue 2, 2019 · pp. 81–87 Read article